FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS

Xingyi Li, Fuxin Li, Xiaoli Fern, Raviv Raich

Abstract: Convolutional neural networks (CNNs) are powerful tools for classification of visual inputs. An important property of CNN is its restriction to local connections and sharing of local weights among different locations. In this paper, we consider the definition of appropriate local neighborhoods in CNN. We provide a theoretical analysis that justifies the traditional square filter used in CNN for analyzing natural images. The analysis also provides a principle for designing customized filter shapes for application domains that do not resemble natural images. We propose an approach that automatically designs multiple layers of different customized filter shapes by repeatedly solving lasso problems. It is applied to customize the filter shape for both bioacoustic applications and gene sequence analysis applications. In those domains with small sample sizes we demonstrate that the customized filters achieve superior classification accuracy, improved convergence behavior in training and reduced sensitivity to hyperparameters.

Conflicts: NA